How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities

This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Improved Decision Tree Method in E-Learning System for Predictive Student Performance System During COVID 19

E-Learning courses are top-rated in recent years. While COVID 19 primarily affects public health, spillover effects can already be observed in education, stemming mainly from extended educational institution closures. This is undoubtedly the critical time for the education sector because, during this period, many universities’ admission exams and competitive examinations are conducted. For them, we should forget about tests, admissions, etc. The need to study student success and forecast their success, along with that is increasing. With the increasing number of it was tested for instructional technology’s popularity, various data mining algorithms perfect for predicting student performance. The right algorithm depends on the algorithm’s nature. A guess has to be made by the faculty. If the number of students, the need to correct data complexity raises data relationship and their processing is an issue for the student to be found at the expense of failure. The decision tree approach to the statistical analysis of academic findings in this paper concerns and the big data implication. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.